Book Image

Hands-On Financial Trading with Python

By : Jiri Pik, Sourav Ghosh
Book Image

Hands-On Financial Trading with Python

By: Jiri Pik, Sourav Ghosh

Overview of this book

Creating an effective system to automate your trading can help you achieve two of every trader’s key goals; saving time and making money. But to devise a system that will work for you, you need guidance to show you the ropes around building a system and monitoring its performance. This is where Hands-on Financial Trading with Python can give you the advantage. This practical Python book will introduce you to Python and tell you exactly why it’s the best platform for developing trading strategies. You’ll then cover quantitative analysis using Python, and learn how to build algorithmic trading strategies with Zipline using various market data sources. Using Zipline as the backtesting library allows access to complimentary US historical daily market data until 2018. As you advance, you will gain an in-depth understanding of Python libraries such as NumPy and pandas for analyzing financial datasets, and explore Matplotlib, statsmodels, and scikit-learn libraries for advanced analytics. As you progress, you’ll pick up lots of skills like time series forecasting, covering pmdarima and Facebook Prophet. By the end of this trading book, you will be able to build predictive trading signals, adopt basic and advanced algorithmic trading strategies, and perform portfolio optimization to help you get —and stay—ahead of the markets.
Table of Contents (15 chapters)
1
Section 1: Introduction to Algorithmic Trading
3
Section 2: In-Depth Look at Python Libraries for the Analysis of Financial Datasets
9
Section 3: Algorithmic Trading in Python

Special Python libraries for EDA

There are multiple Python libraries that provide EDA in a single line of code. One of the most advanced of them is dtale, shown in the following code snippet:

import dtale
dtale.show(valid_close_df)

The preceding command produces a table with all the data (displaying only the first seven columns), as follows:

Figure 2.21 – The dtale component showing spreadsheet-like control over the valid_close_df DataFrame

Figure 2.21 – The dtale component showing spreadsheet-like control over the valid_close_df DataFrame

Clicking on the arrow at the top displays a menu with all the functionality, as illustrated in the following screenshot:

Figure 2.22 – The dtale global menu showing its functionality

Figure 2.22 – The dtale global menu showing its functionality

Clicking on the column header displays each feature's individual commands, as illustrated in the following screenshot:

Figure 2.23 – The dtale column menu showing column functionality

Figure 2.23 – The dtale column menu showing column functionality

Interactive EDA, rather than command-driven EDA, has its advantages...